NOTICE

This document is disseminated under the sponsorship of the U.S. Department 

of Transportation in the interest of information exchange. The U.S. Government 

assumes no liability for the use of the information contained in this document. This 

report does not constitute a standard, specifi cation, or regulation.

The U.S. Government does not endorse products of manufacturers. Trademarks 

or manufacturers’ names appear in this report only because they are considered 

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QUALITY ASSURANCE STATEMENT

The Federal Highway Administration (FHWA) provides high-quality information 

to serve Government, industry, and the public in a manner that promotes public 

understanding. Standards and policies are used to ensure and maximize the 

quality, objectivity, utility, and integrity of its information. FHWA periodically 

reviews quality issues and adjusts its programs and processes to ensure continuous 

quality improvement.

Source: Cover images – Crash, iStockphoto®; VMS sign, Cambridge Systematics, Inc.; Traffic, Digital Stock, a 

division of Corbis Corp.

 Banner images – Aerial highway, Cambridge Systematics, Inc.; Crash, iStockphoto®; VMS sign, Cambridge 

Systematics, Inc.; Traffic, Digital Stock, a division of Corbis Corp.
Technical Report Documentation Page

1. Report No.

FHWA-HOP-12-045

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Analysis, Modeling, and Simulation for Traffic Incident Management 

Applications

5. Report Date

July 2012

6. Performing Organization Code

7. Author(s)

Richard Margiotta, Rick Dowling, and Jawad Paracha

8. Performing Organization Report No.

9. Performing Organization Name and Address

Cambridge Systematics, Inc.

4800 Hampden Lane

Bethesda, MD 20814

10. Work Unit No.

11. Contract or Grant No.

DTFG-61-D-00004

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

Office of Operations (HOP)

1200 New Jersey Ave, SE

Washington, DC 20590

13. Type of Report and Period Covered

Final Report, July 2012

14. Sponsoring Agency Code

HOTO

15. Supplementary Notes

FHWA Contracting Officer’s Task Manager (COTM): Jawad Paracha

16. Abstract

Traffic incidents are a major source of congestion in both urban and rural areas. Nationally, roughly 25 percent of 

total congestion is due to traffic incidents. Further, traffic incidents create unexpected congestion – congestion 

that occurs in times and places where travelers don’t expect to be delayed – and are therefore a major source of 

frustration for travelers.

Implementing Traffic Incident Management (TIM) strategies has proven to be a highly cost-effective way of 

treating congestion problems. However, a strong need exists to be able to predict what the impacts of TIM 

strategies will be at the planning stage of project development and to monitor the effects of TIM programs.

To support modeling and evaluation of TIM strategies, this document provides a synthesis of analysis, modeling, 

and simulation (AMS) methods for incident impacts. The focus is on incidents effects on congestion and reliability 

as well as secondary incidents, for the purpose of estimating benefits and evaluating programs and proposed 

strategies. This document covers several specific topics including: a synthesis of AMS methods for incidents, TIM 

AMS application areas, data required to undertake modeling and evaluations of TIM strategies, and identification 

of future improvements to TIM AMS applications.

17. Key Words 

Traffic incident management, modeling, 

simulation, performance measures

18. Distribution Statement

No restrictions. 

19. Security Classif. (of this report) 

Unclassified

20. Security Classif. (of this page) 

Unclassified

21. No of Pages 

54

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
I

TABLE OF CONTENTS

INTRODUCTION ......................................................................................................................... 1

Purpose of This Document .............................................................................................. 1

Uses for TIM AMS Methods ............................................................................................ 2

How We Developed This Document ................................................................................. 3

SYNTHESIS OF INCIDENT ANALYSIS, MODELING, AND SIMULATION METHODS .............................. 3

Survey of Practitioners on TIM AMS Methods ................................................................... 3

TIM AMS Methods ......................................................................................................... 7

Evaluation of TIM AMS Methods.....................................................................................25

INCIDENT DATA REQUIRED TO SUPPORT TIM AMS APPLICATIONS ...............................................44

TIM Performance Measures ............................................................................................44

Data Collection .............................................................................................................44

Default Data for TIM AMS Applications ...........................................................................47

FUTURE IMPROVEMENTS IN TIM AMS APPLICATIONS ..................................................................50

Topic 1 – Data Management/Data Dictionary for Archiving the Data Needed to Compute 

Incident Performance Measures ......................................................................................50

Topic 2 – Linking Incident Management Strategies to Incident Model Inputs .........................51

Topic 3 – Real-Time Management and Prediction Methods .................................................51

Topic 4 – Identify Key Factors that Cause Secondary Crashes ............................................52

Topic 5 – Modeling Progressive Lane Opening ..................................................................52

Topic 6 – Update of HCM Sections Regarding Incident Management and Capacity Loss ........53

Topic 7 – Estimating Incident Delay and its Contribution to Total Delay ...............................53

Topic 8 – Use of Simulation in Tabletop Exercises ............................................................54

Topic 9 – Framework Development for TIM Program Evaluation ..........................................55

Topic 10 – Expand Benefit/Cost Analysis to Include Secondary Impacts ..............................55

Topic 11 – Resource Optimization for TIM Plans ...............................................................56
II

LIST OF FIGURES

Figure 1. Relative Sources of Congestion ................................................................................... 1

Figure 2. Use of Incident Data by Agencies................................................................................ 5

Figure 3. Useful Applications of Technical Guidance,With “1” Being the Most Important ................. 6

Figure 4. Type of Technical Guidance Desired for Improving TIM AMS .......................................... 7

Figure 5. Incident Data Model for Performance Monitoring ..........................................................45

LIST OF TABLES

Table 1. ICM TIM Modeling Tools ...........................................................................................21

Table 2. TIM Review Matrix ...................................................................................................26

Table 3. Input Requirements of TIM AMS Procedures ................................................................47

Table 4. Lateral Locations of Incidents, Atlanta, Georgia ...........................................................48

Table 5. Incident Duration by Incident Type, Atlanta, Georgia ....................................................49
1

INTRODUCTION 

PURPOSE OF THIS DOCUMENT

Traffic incidents are a major source of congestion in both urban and rural areas. Nationally, roughly 25 

percent of total congestion is due to traffic incidents (Figure 1). Further, traffic incidents create unexpected 

congestion – congestion that occurs in times and places where travelers don’t expect to be delayed – and are 

therefore a major source of frustration for our customers.

Figure 1. Relative Sources of Congestion

(Source: Chin et al., Temporary Losses of Highway Capacity and Impacts on Performance, prepared for FHWA, 

2004, http://www-cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2004_209.pdf.)

In addition to contributing to total congestion, disruptions such as traffic incidents, work zones, and 

inclement weather also define travel time reliability, which is the variability in congestion that occurs from 

day-to-day. This variability, and the uncertainty in travel conditions it causes, has been shown to have 

significant costs to travelers beyond “typical” or “average” congestion. It is therefore critical that the 

reliability component of congestion be captured when performing evaluations and economic analyses of 

transportation investments.

Implementing Traffic Incident Management (TIM) strategies has proven to be a highly cost-effective way 

of treating congestion problems. However, a strong need exists to be able to predict what the impacts 

of TIM strategies will be at the planning stage of project development and to monitor the effects of 

TIM programs.

To these ends, this document provides a synthesis of analysis, modeling, and simulation (AMS) methods 

for incident impacts. The focus is on incidents effects on congestion and reliability as well as secondary 

incidents, for the purpose of estimating TIM benefits and evaluating programs and proposed strategies.
2

The research and examples presented in this document will significantly assist practitioners in the 

planning and development of TIM and the evaluation of the performance of TIM strategies. By estimating 

the expected impacts and evaluating the performance of TIM projects, practitioners can demonstrate 

the value of TIM. Specifically, the research and examples can be linked with best practices in TIM in a 

number of ways:1

1. Methods for measuring incident impacts from field data: these can be used to establish 

performance monitoring procedures based on field data. Collection of incident characteristics and 

the sensitivity of the estimates to the accuracy of incident data reporting also are covered. Travel 

time reliability metrics can be readily derived from field measurements of performance under 

incident conditions. 

2. Methods for predicting impacts of a single incident: practitioners can choose the most appropriate 

tool for the analysis of the problem in hand, from simple queuing models for isolated incidents to 

complex simulation tools on traffic corridors with multiple bottlenecks where the incident location 

within the system is of great importance. 

3. Methods for predicting cumulative incident impacts: systemwide impacts of incidents that can be 

used as a decision support tool in a TMC to trigger actions for the management of the adverse 

impacts of incidents is covered. These include control of ramps or signals, and rerouting of traffic. 

4. Methods for predicting incident duration: given a set of incident characteristics, what is the 

expected duration so more accurate information can be provided in a timely manner and response 

and clearance activities can be deployed. These can be used in conjunction with methods for predicting 

cumulative impacts (#3 above) for possible corridor management and rerouting to parallel facilities.

5. Methods for predicting secondary incidents: better understanding of the occurrence of secondary 

incidents based on the characteristics of primary incidents, the operating conditions of the facility, 

and the TIM in place; guidance in developing and implementing TIM measures to reduce the 

occurrence of secondary incidents. 

USES FOR TIM AMS METHODS 

A wide variety of applications require TIM AMS methods for incidents. These applications can include: 

• Development and evaluation of TIM plans – What is the expected impact of TIM strategies on 

congestion and secondary crashes as a basis for a TIM plan?

• Analysis and evaluation of TIM strategies such as use of service patrols – What effect have TIM 

strategies had after they were implemented?

• Decision support systems used for incident management – What is the expected duration of an 

incident that has just occurred, what will be its impacts on congestion, and what strategies should 

be deployed to manage incidents.

• Congestion/operations performance measurement – What are the trends in incident 

characteristics, how have they been affected by TIM programs, and what role do incidents play in 

total congestion and travel time reliability? 
3

• Benefit-cost analysis of TIM programs/strategies – What congestion and safety benefits result 

from TIM programs?

• Integrated Corridor Management – How can the response of other corridor management methods 

be integrated with TIM strategies?

HOW WE DEVELOPED THIS DOCUMENT 

In developing the material presented in this document, we reviewed the relevant technical literature as a 

starting point. We then conducted a survey of practitioners and researchers to identify the current state 

of the practice in AMS for incidents. The progress of the work was monitored by an independent threeperson review panel.

SYNTHESIS OF INCIDENT ANALYSIS, MODELING, 

AND SIMULATION METHODS

This section describes TIM AMS methods and related applications. In each of the following subsections 

the current state of the practice is documented. The state of the practice (what is in current use) is 

discussed separately from the state of the art (that what has been researched). 

SURVEY OF PRACTITIONERS ON TIM AMS METHODS

A TIM AMS survey was sent to state departments of transportation (DOT) and metropolitan planning 

organizations (MPO) in August 2011. The purpose of this survey was to determine current practices in 

TIM AMS, and to identify areas where practitioners felt that additional guidance would be valuable. The 

survey questionnaire included eight questions related to TIM:

1. Have you ever conducted a study of the incident impacts on congestion (e.g., delay due to 

incidents)? If so, please attach the relevant study in an e-mail.

2. Have you ever conducted a study of secondary crashes due to incidents? If so, please attach the 

relevant study in an e-mail.

3. Do you routinely measure and report secondary crashes?

4. What software tools have you either developed or used to estimate incident congestion impacts or 

secondary crashes?

5. In what applications that use incident data does your agency currently engage? 

6. For which applications would technical guidance be most helpful to you? 

7. What kind of information/data would be helpful to you in “making the case” for incident 

management programs internally with your agency?

8. In terms of your needs for incident information, what types of technical guidance would help you the most? 
4

Eleven agencies responded by September 2011; they are: 

• Delaware Valley Regional Planning Commission (DVRPC);

• Florida DOT, District 6;

• Indiana DOT;

• Kansas City Scout (Kansas DOT and Missouri DOT);

• Maryland State Highway Administration;

• Missouri DOT;

• New Hampshire DOT;

• Regional Transportation Commission (RTC) of Southern Nevada;

• Rhode Island DOT;

• Southeast Michigan Council of Governments; and

• Washington State DOT.

Study of Incident Impacts on Congestion

Out of the 11 agencies who responded the survey, 5 (or 45 percent) indicated that they have conducted 

studies of the incident impacts on congestion (e.g., delay due to incidents). 

Study of Secondary Crashes

Out of the 11 response, 4 (or 36 percent) indicated that they have conducted studies of secondary 

crashes due to incidents. 

Measuring and Reporting Secondary Crashes

Out of the 11 response, 5 (or 45 percent) indicated that they routinely measure and report secondary crashes. 

Software Tools for Estimating Incident Congestion Impacts/Secondary Crashes

Out of the 11 response, 8 (or 73 percent) indicated that they have either developed or used software 

tools to estimate incident congestion impacts or secondary crashes.

Use of Incident Data

Figure 2 shows the applications of incident data by the surveyed agencies. The top five applications are: 
5

• Analysis and evaluation of TIM strategies such as use of service patrols;

• Real-time traveler information dissemination;

• Development and evaluation of TIM plans;

• Agency performance reports; and

• Incident prediction and detection.

Figure 2. Use of Incident Data by Agencies

(Source: Cambridge Systematics, Inc.)

Useful Applications of Technical Guidance

Figure 3 shows the useful applications of a TIM technical guidance (with 1 being the most important for 

TIM AMS application). The top five areas include: 

• Relationship between TIM and overall congestion/travel time reliability;

• Development and evaluation of TIM plans;

• Relationship between TIM and overall safety;

• Safety analysis applications such as secondary crash analysis; and

• Analysis and evaluation of TIM strategies such as use of service patrols.
6

Figure 3. Useful Applications of Technical Guidance 

With “1” Being the Most Important

(Source: Cambridge Systematics, Inc.)

Information/Data Helpful for TIM Programs

When being asked what kind of information/data would be helpful for them in “making the case” for TIM 

programs internally with their agency, the respondents provided the following answers:

• Reliable systemwide speed data.

• Better information related to the benefits of delay/congestion management through transportation 

systems management and operations.

• Data for reducing congestion and improving safety and linking it with departments such as 

Operations, Planning, and Safety and Security.

• Injury information is collected for events in which the Service Patrol responds to within the 

District. But to quantify how many incidents have been averted because of Service Patrol or 

notification of an incident due to posting of a Dynamic Message Sign (DMS) or through the state 

511 system would be helpful information.

• Benefit/cost data, and how incident management is directly tied to safety performance measures.

6
7

• Benefit/cost data; find a way to get public officials to understand benefits so they can 

communicate to media and traveling public; work with upper management so they can present 

benefit to their board of elected officials.

• Anything would be helpful in justifying operating and maintaining a TMC.

• What can we use to show the cost of secondary crashes and convince our planners and designers to take 

into consideration traffic safety/congestion/secondary crashes when they design TIM plans for projects.

• Programs to track incident clearance and closure time that can be integrated with control systems

• Documented safety and congestion improvement results of having a program in place.

Desired Technical Guidance 

Figure 4 shows the types of technical guidance that would be helpful. The top two types are technical 

methods for predicting incident congestion extent (e.g., delay, reliability) and technical methods for 

predicting incident duration. 

Figure 4. Type of Technical Guidance Desired for Improving TIM AMS

(Source: Cambridge Systematics, Inc.)

TIM AMS METHODS

This subsection documents the TIM AMS methods and their applications as revealed through a review of the 

recent literature as well as through agency contacts. It includes what currently is being used by practitioners 

and what is available from the research. Five types of approaches to evaluating TIM are described:
8

1. Methods for measuring incident impacts from field data; 

2. Methods for predicting impacts of a single incident;

3. Methods for predicting cumulative incident impacts;

4. Methods for predicting incident duration; and

5. Methods for predicting secondary incidents. 

Methods for Measuring Incident Impacts from Field Data

Methods for measuring in the field the impacts of incidents on delay must address three challenges: 

• The definition of what constitutes delay; 

• Collection of delay data over extended periods of time; and

• The parsing of the observed delay among incidents and various other possible causes of delay.

Delay is often defined as the difference between the actual travel time and the free flow travel time. 

Field measurement of travel time over extended periods has historically been difficult, so agencies and 

researchers have resorted to spot speed measurements over extended lengths of the facility to compute 

approximate delays. 

State of the Practice

Congestion Monitoring Systems

Congestion monitoring systems and programs are either in place or being developed in the major urban 

areas of the U. S. Monitoring is done using permanent spot speed measurement stations on freeways, 

targeted field measurements using floating cars of specific facilities, and/or the use of GPS/cell phone 

tracking devices by commercial vendors of real-time congestion data. However, the assignment of 

causality to the measured congestion, and the attribution of delay to incidents are extremely rare in current 

practice. Assignment of causality is more often done as part of specific research efforts.

One example of how an agency defines delay over extended periods is Caltrans.2 Caltrans defines two 

delay values: the difference between the observed spot speed and 60 mph (free-flow delay), and the 

difference between the observed spot speed and 35 mph (breakdown delay). The two definitions of 

delay are used because the agency’s goal is to minimize breakdown delay.

Automated permanent vehicle detector stations approximately a half-mile apart on urban freeways are 

used by Caltrans to measure five-minute average spot speeds (24 hours per day, 7 days per week). The 

vehicle-hours of delay measured at each station is the actual volume measured at the station multiplied 

by the difference in travel times between detector stations at the actual speed and the delay threshold 

speed (either 35 mph or 60 mph):

 Delay = Volume * [(Length/actual speed) – (Length/threshold speed)]

Caltrans currently does not parse the observed delay into various causes, but has plans to do so in future 

editions of its statewide Mobility Performance Reports.
9

The TTI 2011 Urban Mobility Report is an example of a national congestion monitoring report which tallies 

total delay associated with all causes of delay, but does not assign responsibility to any specific causes, such 

as incidents. Delay is measured by tracking vehicle GPS devices and comparing off-peak to peak travel times.

State of the Art

Kwon et al. used quantile regression to apportion the causes of measured congestion between incidents 

and other causes.3 In essence, the maximum likelihood contribution of incidents to measured delay is 

estimated through least squares regression. No underlying traffic behavior model is required.

Skabardonis et al. used a more legalistic approach to separating out incident-related congestion from 

other congestion.4 First the incident logs were consulted to identify nonincident days. These became the 

baseline congestion days. These days were then compared to days with incidents. The difference in delay 

between incident days and incident-free days was considered to be the delay associated with incidents.

The statistical and legalistic approaches are somewhat unusual. More typical are traffic model-based 

approaches such as List et al. which used the classical queuing model in New York State DOT’s 

Congestion Needs Assessment Model and updated look-up tables of key parameters to estimate the 

amount of congestion on arterial streets that might be attributable to incidents.5 This approach (like 

all traffic model-based approaches) requires demand volumes during the incidents and the estimated 

capacity of the facility before, during and after the incident is present. The estimated delays produced by 

all of the incidents over the year are summed to obtain incident delays for the year. 

The Urban Mobility Report: Improved Data Leads to Better Understanding of Congestion 

The Texas Transportation Institute has been publishing the Urban Mobility Report (UMR) annually since 

1987. Up until 2010, congestion was estimated – rather than measured – using traffic count and 

roadway characteristics data with analytic methods. Starting with 2010, travel time data collected by a 

private vendor from GPS-equipped vehicles has been used. As noted in the 2011 UMR:

The new data and analysis changes the way the mobility information can be 

presented and how the problems are evaluated: 

• Hour-by-hour speeds collected from a variety of sources on every day of 

the year on most major roads are used in the 101 detailed study areas 

and the 338 other urban areas. 

• The data for all 24 hours makes it possible to track congestion 

problems for the midday, overnight, and weekend time periods.

• A new wasted fuel estimation process was developed to use the more 

detailed speed data.

• The effect of TIM strategies and other operational treatments on congestion are now considered.

(Source: http://tti.tamu.edu/documents/mobility-report-2011.pdf.)
10

Methods for Predicting Impacts of a Single Incident

There are a variety of general purpose and incident-specific analytical tools for predicting the delay 

impacts of a given incident on a given facility. Since the incident is “given,” these models do not focus 

on predicting the time, location, and type of incident. They focus on predicting the consequences. 

Incident-specific analytical tools also may predict secondary incidents and the duration of the incident.

General purpose tools include traffic simulation models, and the recently published 2010 Highway 

Capacity Manual (HCM) method for freeways. Both facility-specific and systemwide impacts of individual 

incidents can be estimated using microsimulation models, and mesoscopic simulation models. The 2010 

HCM currently is limited to single facility applications.

Microsimulation models require more data and will tend to produce more precise estimates of delay effects 

than mesoscopic models. Mesoscopic models require more precise facility design (and operations) data 

than demand models, and tend to produce more precise estimates of delay effects than demand models. 

Some of the models specifically tailored to the evaluation of incidents can predict incident duration and 

secondary incident occurrence as well as delay. General purpose models require this information as input 

and cannot predict these parameters.

State of the Practice – General Purpose Traffic Operations Analysis Models 

General purpose traffic operations analysis models are “state of the practice” for the estimation of the 

delay effects of specific incidents with a given duration. They do not predict incident duration or the 

probability of secondary incidents.

There are numerous instances in the literature of general purpose traffic operations analysis models being used 

in research and in practice to predict the delay effects of incidents and incident management strategies. General 

purpose traffic operations analysis models of specific incidents come in two basic types: Highway Capacity 

Manual and Simulation (micro and mesoscopic). (See Volumes 1 and 2 of the FHWA Traffic Analysis Toolbox.)6 

HCM-Based Deterministic Macroscopic Analysis Tools

Overview of Deterministic Tools

Analytic tools predict road traffic capacity, speed, delay, and queuing at intersections and road segments of a 

variety of types and configurations. Many, though not all, of these tools are based on methodologies published in 

the Highway Capacity Manual (HCM).

The incident type, start time, location, and duration must be specified. Chapter 10 of the 2010 Highway Capacity

Manual then provides special capacity adjustment factors for incidents on freeways. It covers breakdowns and

collisions that occur on the shoulders, as well as incidents that block one, two, or three lanes on freeways with two 

to eight lanes in one direction. The methodology described in Chapter 10 is then used to predict the mean speed (and 

therefore travel time and delay) and density of traffic on the freeway before, during, or after an incident. 

Similar information on capacity and performance effects is not available in the 2010 HCM for urban streets.

What is this tool used for?

HCM-based tools are used to evaluate the effects of traffic operations on isolated transportation facilities such as 

signalized and unsignalized intersections, freeway mainline segments, freeway weaving segments, freeway ramp 

merge/diverge areas, and others.
11

Overview of Deterministic Tools (continued)

Why is this tool favored for such uses?

The HCM is widely used and its results are accepted (near universally) throughout the industry as representative of 

actual conditions. Many jurisdictions employ performance standards and thresholds that have been developed with 

HCM level of service measures in mind. 

The HCM is often viewed as the benchmark for evaluating traffic operations. It has a long and storied history 

of use and acceptance in the industry. Additionally, analysts and decision-makers appreciate the nature of its 

analytical procedures in terms of consistency of inputs, algorithmic calculations, and resulting output values.

What are challenges and limitations of this tool?

Many users comment that deterministic tools do not properly evaluate the relationships between adjacent or other 

interacting facilities/control devices, i.e., network affects are ignored. These tools assume demand is fixed and unaffected

by highway improvements. Usually the demand model used to forecast demand takes into account approximate effects of 

highway improvements. The HCM is then used to determine more precise effects for given demand.

Deterministic methodologies are sensitive to the length of the analysis periods. HCM 2000 methodologies do 

not account for the variation in traffic states within an analysis time period. HCM 2010 freeway methodologies 

incorporate time slices in a manner similar to macroscopic simulation which addresses variations in traffic states.

Software implementations of the HCM often incorporate default values which make it easy to produce results 

without giving proper consideration to detailed characteristics of the conditions being evaluated.

When considering the limitations it is important to distinguish between the limitations of the various software 

implementations and those of the actual HCM. Additionally, certain limitations can be overcome through iterative 

applications of deterministic tools.

Some deterministic tools do not employ methods documented in the HCM. The comments pertaining to HCM tools 

apply to these tools for the most part as well, except that these tools are not as widely used and so the likelihood 

of inconsistency is increased with analysis tools used ‘upstream’ or ‘downstream’ in the analytic process.

What are some software implementations of this tool?

Software examples of deterministic tools include HCS, Teapac, TRAFFIX, RODEL, and SIDRA.

Example Applications:

Skabardonis, A., and M. Mauch, FSP Beat Evaluation and Predictor Models: Methodology and Documentation, 

Research Report UCB-ITS-RR-2003-XX, University of California Berkeley, 2003 (Updated 2010).

Hagen, L., H. Zhou, and H. Singh, “Road Ranger Benefit Cost Analysis,” Center for Urban Transportation Research, 

University of South Florida, November 2005.

The first report describes the development and documentation of deterministic spreadsheet-based tools for estimating 

the benefit/cost ratio of freeway service patrols (FSP) on a freeway site (“beat”). FSP is an incident management 

measure designed to assist disabled vehicles along congested freeway segments and reduce nonrecurring congestion 

through quick detection and response to accidents and other incidents on freeways. The benefits of FSP depend 

on the beat’s geometric and traffic characteristics, and the frequency and type of assisted incidents. The models, 

implemented into spreadsheets, calculate the savings in incident delay, fuel consumption and air pollutant emissions 

based on data that are commonly available to local agency operations staff. The report includes step-by-step 

instructions for applying the models and analyzing the results. The models have been independently applied by 

operating agencies in Virginia, Florida, and Hawaii to evaluate the effectiveness of their FSP programs. 
12

Simulation Tools

Simulation Tools: Microsimulation Tools

Microscopic simulation models simulate speed and traffic density by tracking the instantaneous movement of 

individual vehicles through the network based on a statistical distribution of arrivals and driver behaviors.

The start time, location, lanes blocked, and duration must be specified for each incident. Merging effects are 

automatically accounted for in microsimulation models; however, distraction effects on the capacity of remaining 

lanes (rubbernecking) must be specified by the analyst.

What is this tool used for?

As with macroscopic tools, microsimulation tools are used to evaluate changes in operation on a facility that result 

from changes in demand, capacity, or traffic control. Microscopic tools simulate traffic on a quantum time scale 

(less than one second) based on the movement and spacing of individual vehicles. Therefore microscopic tools can 

be used to evaluate the interaction between different vehicles and between vehicles and individual controls and 

capacity constraints. Furthermore detailed microsimulation models can evaluate the instantaneous and cumulative 

effects of small changes to facility geometry and timing.

Why is this tool favored for such uses?

Well-calibrated microsimulation tools are superior to other tools for evaluating the sensitivity of operations to small 

changes. Microsimulation tools allow analysts to identify capacity constraints and opportunities for improvements 

more precisely. Microscopic tools permit the evaluation of assumptions about driver behavior in addition to 

management and operational strategies.

Microscopic tools can be incorporated in planning, design, and systems management and provide robust feedback 

reflecting the cumulative systemwide effects of local modifications and improvements.

Many of the microsimulation tools are packaged with state-of-the-art animation and graphics capabilities. Given 

the focus of microsimulation tools on individual vehicles, this provides analysts with a convenient and persuasive 

means of communicating the local and systemwide implications of analysis results.

What are challenges and limitations of this tool?

Microsimulation tools can be prohibitively expensive to implement. The level of detail of a microsimulation model 

is the direct cause of this. The input data requirements of complex microsimulation models can easily exceed data 

availability, resulting in the widespread use of defaults. Furthermore, microsimulation models are acutely sensitive 

to proper calibration and the results generated by the inappropriate use of default inputs can vary considerably from 

results generated using properly calibrated microsimulation models.

Effective use of microsimulation tools requires a considerable amount of training and quality control. The 

generation of poor quality analysis is often facilitated by the proliferation of defaults, leading to some confusion 

over what constitutes calibration.

Microsimulation tools treat origin-destination patterns as fixed inputs. Induced demand is not evaluated though 

traffic diversion can be evaluated using some microsimulation tools when complete alternate routes are represented 

within the geographic scope being modeled. Considerable time and training are required for the development of 

complex models using microscopic tools.

Microsimulation tools require multiple analyses and the results should be averaged. This requirement is due to 

the variations in results caused by use of random number generators for the starting point of the analysis. Even 

if the same starting point is used for the analysis, different results may be obtained from the use of different 

simulation platforms. 

What are some software implementations of this tool?

Software examples of Microsimulation tools include CORSIM, VISSIM, SimTraffic, AIMSUN, Paramics, Dynasim, 

and Transmodeler.
13

Simulation Tools: Microsimulation Tools (continued)

Example Application:

Chou, C-S, and E. Miller-Hooks, “Exploiting the Capacity of Managed Lanes in Diverting Traffic Around an Incident,” 

Transportation Research Record: Journal of the Transportation Research Board, #2229, 2011.

Evaluation of the potential benefits and detriments of diverting general traffic into a managed lane when an incident 

arises along the general purpose lanes using the VISSIM microscopic simulation tool. Continuous and access point 

diversion strategies were evaluated regarding their impacts on the mobility of general traffic and managed lane 

users along a concurrent flow lane system on I-270 in Maryland.

Simulation Tools: Mesoscopic Simulation Tools

Mesoscopic simulation models combine properties of both macro- and micro-scopic models.

Many of these models employ multiresolution demand and network modeling with dynamic traffic assignment 

(DTA) and selected subarea simulation to predict how system performance and traffic demand will vary in response 

to an incident. The analyst inputs the incident location, capacity reduction, and duration.

What is this tool used for?

Mesoscopic tools are a relatively new addition to the traffic analysis toolbox. Mesoscopic tools combine a focus on 

individual vehicles and drivers (as with microscopic models) with average measures of speed and density (as with 

macroscopic models). They are used to evaluate the systemwide effects of changes to driver behavior and performance

on individual approaches and segments. Incidents can be directly coded into the network and their effect determined. As 

a result mesoscopic tools have been recommended for use in planning the operations over citywide or regional networks. 

Why is this tool favored for such uses?

They can dynamically assign traffic based on the performance of specific facilities in a network, unlike macroscopic 

models, but they cannot compare to the precision of microsimulation models, which focus on the behaviors of 

individual drivers/vehicles.

Mesoscopic models can address a variety of traffic adaptations to network changes, including route shifts and 

changes in departure times. In the latter sense, mesoscopic tools come closer to addressing induced demand.

Mesoscopic models are easier to use than microscopic models in developing models of large geographic scales. 

Mesoscopic models are more flexible than macroscopic tools for evaluating different facility types within the same model.

A number of software applications exist to facilitate integration between mesoscopic tools and travel demand 

models on the one hand, and microscopic tools on the other.

What are challenges and limitations of this tool?

The implementation of dynamic assignment in mesoscopic tools requires a considerable investment in calibration. 

Minor changes in origin and destination and departure-time patterns can have profound results on the simulated 

performance of alternative routes.

Mesoscopic tools can provide a misleading level of detail about individual link performance given that they are 

typically calibrated to generate analysis of large area networks. This is exacerbated by the simplistic representation 

of signals and other traffic controls.

The sensitivity of mesoscopic tools to default assumptions about driver behavior can be obscured somewhat by the 

use of average values for link-level vehicle speeds and densities.

Mesoscopic simulation models are more susceptible to failure in reaching convergence or equilibrium due to the 

additional interaction of dynamic route assignment with the random properties common to many simulation tools.

Considerable time and training are required for the development of complex models using mesoscopic tools. 
14

Simulation Tools: Mesoscopic Simulation Tools (continued)

What are some software implementations of this tool?

Software examples of mesoscopic tools include DYNASMART-P, DYNAST, CUBE Avenue, Dynameq, and TRANSIMS.

Example Applications:

Fei, X., S. Eisenman, H.S. Mahmassani, and X. Zhou, “Application of DYNASMART-X to the Maryland CHART 

Network for Real-Time Traffic Management Center Decision Support,” Proceedings of the 12th World Congress on 

Intelligent Transport Systems, San Francisco, California 2005.

Application of DYNASMART-X, a simulation-based real-time network traffic estimation and prediction system based 

on dynamic traffic assignment (DTA) methodology, to the CHART network in Maryland. The application considers 

the I-95 corridor network between Washington, D.C. and Baltimore. The CHART network application allows use 

of the prediction and estimation procedures in conjunction with real-time information to consider multiple traffic 

management strategies and scenarios in real time. This can improve the ability of the traffic management center 

to respond to unfolding situations, including incidents, congestion and other unexpected events, through provision 

of traffic information to travelers and deployment of various control measures. The capabilities and benefits of the 

system are illustrated through scenario analysis and evaluation that considers real-time information in the context of 

multiple alternative management strategies in response to the occurrence of an incident on the main traffic facility.

Lili Lou, Examination of Traffic Incident Management Strategies via Multi-Resolution Modeling with Dynamic Traffic 

Assignment, 2012 Transportation Research Board Annual Conference, Conference CD-ROM, 2011.

Lou demonstrated the use of Dynus-T to model various traffic management strategies during a major freeway crash 

in the Phoenix region. In their analysis they imported their inputs form the regions travel demand model, conducted 

analysis in Dynus-T and then exported the output to VISSIM.

State of the Art – Incident-Specific Traffic Operations Analysis Models

Incident-specific traffic operations analysis models are “state of the art,” seeing application primarily in 

research settings.

The iMIT model is an example of a traffic incident-specific traffic operations analysis model (Khattak).7 This tool 

uses statistical models for incident duration and secondary incident occurrence, and uses a theoretically based 

deterministic queuing model to estimate associated delays. It has been tested in Hampton Roads, Virginia.

AIMSUN ONLINE is an example of a simulation model designed to support real-time incident management decisionmaking.8 AIMSUN ONLINE deduces the current traffic status on the streets and the actual demand based on data 

from permanent detectors. With control plans changing dynamically during the day, AIMSUN ONLINE also reads the 

current control plan operated at each network intersection. Parallel simulation runs are conducted to assess a variety 

of possible actions that might be applied in order to improve the network situation compared to the “do nothing” case. 

Methods for Predicting Cumulative Incident Impacts

In addition to modeling the effect of a single incident, it also is desirable to know what the cumulative 

effect of incidents is: this accounts for the variability in incident occurrence and severity that occurs 

over the course of a year. 

The tools for predicting cumulative benefits of incident management fall into three categories:

• Tools that predict the effects of incident management for large systems with minimal details or 

specifics on incident management methods. These tools are typically sketch planning models.
15

• Tools that predict the effects of incident management for single facilities with a great deal 

of detail on the specifics of the incident management methods. These tools are typically 

microsimulation models but with ongoing advances in Highway Capacity Manual methods, may 

soon include HCM analysis tools.

• Tools that predict the effects of incident management for multiple facility systems with moderate 

information on the specifics of the incident management methods. These tools are typically 

mesoscopic simulators employing dynamic traffic assignment.

Sketch planning models are designed to work at very large geographic scales and forecast the 

system effects of a variety of traveler information, demand management, capacity, and operational 

improvements, including incident management.

Overview of Sketch Planning Tools9

Sketch-planning tools are typically simple, low-cost analysis techniques, employing highly aggregated and readily 

available data.

What is this tool used for?

Sketch planning tools are used to provide a quick analytic response to questions about planning concepts and 

alternatives. Sketch planning tools provide an introduction into the analytic process and can be used to communicate 

planning relationships and the effects of background trends. Sketch planning tools can be used to rule out scenarios.

Sketch planning tools support experimentation with alternatives and allow for comparisons between large 

geographic contexts with a minimum investment in set up and analysis.

Sketch planning tools are useful for screening planning alternatives. By incorporating knowledge about cause 

and effect, and costs-benefits into an automated framework, sketch planning tools offer analytic support 

for the initial stages of project development with clarity and robustness that surpasses the use of traditional 

‘rules of thumb.’

Why is this tool favored for such uses?

Sketch planning tools are inexpensive to develop or acquire. Knowledge of basic policy evaluation concepts and 

off-the-shelf software makes learning sketch planning tools and applying them easier and less expensive than 

most other tool types. Sketch tools provide an important benchmark for comparison with subsequent analysis 

results. It is not the case that sketch planning tools are always wrong and travel demand model analysis is always 

right when there is a disagreement in their results. Disagreement between sketch planning tools and other tools 

can be used to prompt a check of the assumptions used with more detailed tools.

What are challenges and limitations of this tool?

Sketch planning tools would benefit from improvements in presentation capabilities. Sketch planning tools often 

do not generate publishable reports and rarely generate graphical information. 

Sketch planning results generally lack precision. Their simplicity is directly related to reliance on a limited number of 

inputs. The validity of results depends on a constrained range of variation among these inputs, which typically do 

not extend far beyond the central tendencies established by past experience. Alternatives and scenarios that reflect 

conditions not measured by the inputs can generate indefensible results. Sketch planning tools are not sensitive to 

operational features of the project (e.g., signal timing) because they do not represent facilities with resolution.

It should be recognized that the low cost of sketch planning tools might be lost in any tradeoff to enhance 

their capabilities.

What are some software implementations of this tool?

Software examples of sketch planning tools include HERS, IDAS, SMITE, SPASM, STEAM, and TELUS.
16

Overview of Sketch Planning Tools (continued)

Example Applications:

The Hampton Roads Planning District Commission (HRPDC) MPO used IDAS to quantify the emissions reductions 

due to reduced incidents as a result of ITS technologies deployed in the Hampton Road region. The IDAS tool was 

used in combination with the regional travel demand model to estimate the daily incremental emission impacts. 

The analysis results “showed a substantial decrease in the daily emissions for hydrocarbons (HC) and NOx

 in the 

region due to the ITS deployment.”

Mesoscopic models, HCM methods, and microsimulation models are generally used to predict the impacts 

of specific incidents, but when combined with scenario generators and applied systematically to a variety 

of possible incident scenarios, these more computationally intensive tools can produce predictions of the 

cumulative benefits of incident management.

More information on these types of tools can be found in Volumes 1 and 2 of the FHWA Traffic 

Analysis Toolbox.10

Sketch Planning Tools

Sketch planning models such as HERS,11 IDAS,12 and TOPS-BC are designed to work at very large 

geographic scales and forecast the system effects of a variety of traveler information, demand 

management, capacity, and operational improvements, including incident management. They apply 

average incident frequencies, average incident durations, and relatively simple speed-flow relationships to 

estimate systemwide, long-term effects of incidents on system demand and system delay.

Title: Incident Response Evaluation: Phase 3

Objective: This study was intended to improve the understanding of the benefits from Incident Response 

(IR) actions by Washington State Department of Transportation (WSDOT). The key objectives of this 

study were to analyze the impacts of incident response service measures on traffic conditions and to 

develop a methodology to help WSDOT more effectively deploy the Incident Response resources. 

Type of Tool/Analysis Used: A variety of statistical analyses of incident data in the Puget Sound region 

in Washington State were performed to investigate how incidents and incident characteristics affect 

roadway performance. 

Results: For the 2006 study year, a conservative estimate was that crashes and other traffic incidents 

cost travelers 5,300,000 vehicle-hours of delay, in addition to typical congestion delay, on the Puget 

Sound region’s freeway system. That was roughly 30 percent of the total delay from all causes that 

occurred on these roadways. It was recommended that roadway segments (5- to 7-mile stretch) that 

produce roughly 45 crashes per year in one direction of travel would exhibit enough savings in travel time 

from incident response to warrant the deployment of incident response on the basis of travel time savings 

alone. The incident response activities were only financially warranted during times when volumes 

exceeded a V/C ratio of 0.6 on two-lane (in one direction) roadways or 0.7 on three-lane or larger roads.
17

HERS

The Highway Economic Requirements System (HERS) is a model for determining optimal highway 

investment programs and policies. Two versions of HERS exist, one for national-level analyses, the 

other, HERS-ST is targeted to state DOT-level analyses. HERS does not have a built in network traffic 

operations analysis module. This information must be provided to HERS from a separate model, such 

as a travel demand model network or a mesoscopic model network. The traffic operations effects 

of different investments in incident management programs are modeled off-line and the results input 

into HERS.

While numerous operations strategies are available to highway agencies, a limited number are now 

considered in HERS (based on the availability of suitable data and empirical impact relationships). 

The types of strategies analyzed can be grouped into four categories: arterial management, freeway 

management, incident management, and travel information.13 For incident management, HERS can 

evaluate the following strategies for freeways only:

• Incident detection (free cell phone call number and detection algorithms);

• Incident verification (surveillance cameras); and

• Incident response (on-call service patrols).

HERS was used to model incident management effects for FHWA’s 2008 Status of the Nation’s 

Highways, Bridges, and Transit: Conditions and Performance Report.

14

More information on HERS can be found at: 

http://www.fhwa.dot.gov/infrastructure/asstmgmt/hersfact.cfm.

HERS: The Oregon Experience15

In 1999, the State of Oregon developed a customized version of the FHWA Highway Economic 

Requirements System (HERS) tool for use in conducting investment analysis in the State. The 

State developed the tool so it could develop more credible estimates of user costs and benefits 

from transportation improvements. When controversy arose in quantifying the costs of delay in a 

high-profile incident on one of the state highways, the State developed additional postprocessors 

to produce estimates of “Unexpected Delay” and “Cost of Unexpected Delay” from the 

HERS-OR tool. 

In a major incident that closed a portion of I-5 for 13 hours, a local newspaper cited estimates of 

user costs that did not match official ODOT estimates from the HERS-OR model. ODOT took the 

opportunity to develop additional postprocessors to HERS-OR that produced an Unexpected Delay Map 

and the Cost of Expected Delay that were acceptable and consistent. This information was shared 

with all departments and the public and now provides a single consistent source for quantifying delay 

within ODOT. 
18

IDAS

The ITS Deployment Analysis System (IDAS) is software that can be used in planning for Intelligent 

Transportation System (ITS) deployments. State, regional, and local planners can use IDAS to estimate 

the benefits and costs of ITS investments – which are either alternatives to or enhancements of 

traditional highway and transit infrastructure. IDAS can predict relative costs and benefits for more than 

60 types of ITS investments. The IDAS ITS components deployed include: 

• Incident detection only; and 

• Both incident detection and incident response. 

IDAS was utilized to model incident management system in Hampton Roads, Virginia.16 The results 

showed a substantial decrease (9 to 14 percent reductions) in the daily emissions for hydrocarbons (HC) 

and NOx

 in the region for the two options containing incident management improvements when compared 

with the control alternative without any improvements. 

More information on IDAS can be found at: 

http://idas.camsys.com/.

TOPS-BC

FHWA’s Office of Operations sponsored this project to provide guidance on conducting benefit/cost 

analysis for operations projects, including incident management. The project developed an Operations 

Benefit/Cost Analysis Desk Reference as well as software to implement it (TOPS-BC). The benefits of 

operations projects include those related to travel time reliability. For the impacts of incident management 

strategies, TOPS-BC uses the IDAS procedures.

SSP-BC

Objective:

The SSP-BC Tool was developed for the I-95 Corridor Coalition and FHWA to fill the need for a 

comprehensive, cost-effective, and standardized Benefit/Cost (B/C) ratio estimation methodology to 

facilitate evaluation of existing Service Safety Patrol (SSP) programs throughout the country. The tool 

is based on commonly accepted assumptions and uses an updateable monetary conversion process. A 

major strength of the tool is not only its utility for evaluating existing programs, but also its applicability 

in testing numerous what-if scenarios, including the introduction of a new program or the impact of 

improvements in service response times.

Methodology: 

Data in tables used in the tool were derived directly from simulation run results (travel delays, fuel 

consumption), regression-based estimates (fuel consumption), a novel hybrid statistical-simulation data 

methodology with improved model fitness (travel delay), computations (emissions, secondary incidents), 

and from publically available sources (wages, fuel costs, traffic composition, and monetary conversion 
19

rates). Power-based equations that incorporate vehicle characteristics and modal parameters (vehicle 

mass, velocity, and acceleration) in computing instantaneous power demand for each vehicle type 

category are used in the estimation of fuel consumption and emissions produced (carbon dioxide (CO2), 

carbon monoxide (CO), methane (CH4), Nitrogen Oxides (NOx

), and Sulfur Oxides (SOx)). The equations 

are responsive to roadway geometry, traffic volume, grade, and other characteristics of the traffic 

environment. The output of the tool includes the B/C ratio incorporating user-specified benefit measures, 

savings in travel delay in vehicle-hours, fuel consumed by passenger cars and light-duty vehicles in 

gallons, number of prevented secondary incidents, and emission pollutants in metric tons.17

Example Applications

Evaluation of Emissions Impacts of an Incident Management System in Hampton Roads, Virginia

The Hampton Roads Planning District Commission (HRPDC) MPO had invested in deploying ITS 

technologies in the Hampton Road region. They believed the reduced incidents due to the incident 

management put in place during the ITS program should logically lead to reductions in emissions. 

They wanted a tool that could be used to estimate and quantify the emissions reduction. The IDAS 

software was selected to conduct analysis to quantify the expected emissions reduction. The 

HDRPC also was interested in quantifying any emissions reductions to the EPA, FHWA, and Air 

Quality Bureaus to be used in determining the region’s air quality conformity status. 

Output runs for a base case and two 2021 scenarios from the regional travel demand model were 

fed into the IDAS tool for analysis. The IDAS tool was used to estimate the daily incremental 

emission impacts.

The analysis results “showed a substantial decrease in the daily emissions for hydrocarbons (HC) and 

NOx in the region for the two options containing incident management improvement when compared 

with the control alternative.” Run 1 was the current and near-term ITS deployments, while Run 2 

represented greater regional incident management, planned for the future. Though EPA did not 

ultimately use the results it was a first step for HRPDC to begin quantifying the benefits of their 

incident management program.

CHART 

The Coordinated Highways Action Response Team (CHART) is a joint effort of the Maryland 

Department of Transportation, Maryland Transportation Authority, and the Maryland State Police. 

Its mission is to improve real-time operations of Maryland’s highway system through teamwork and 

technology. From February 2001, all incident requests for emergency assistance have been recorded 

in the CHART information system and this has significantly enriched the available incident data. 
20

The University of Maryland, as part of the ongoing CHART evaluations, developed a predictive equation 

model based on running experiments with microscopic simulation:

Excess Delay Due to Incidents = e(-10.19 * (V)2.8 * (NLB/TNL)1.4 * (ID)1.78)

 Where: TNL = Total number of lanes;

 NLB = Number of lanes blocked;

 V = Traffic volume; and

 ID = Incident duration.

Using this model, it was determined in 2009 that the CHART program reduced delays by 32.43 million 

vehicle-hours. 

More information on CHART can be found at: 

http://www.chart.state.md.us/.

Scenario-Based Modeling Approaches

Scenario-based modeling approaches apply mesoscopic models, HCM methods, and microsimulation 

models repeatedly to a variety of possible incident conditions to arrive at an assessment of the 

cumulative effects of incident management. There are three primary examples of this approach: The 

Integrated Corridor Management (ICM) analysis, the FHWA ATDM Evaluation Guide, and the SHRP 2-L08 

Reliability in HCM project.

Integrated Corridor Management Analyses

The evaluations of Integrated Corridor Management strategies for Minneapolis, Dallas, and San Diego 

used travel demand models, mesoscopic simulation models and microsimulation analysis in combination 

with selected incident scenarios (Table 1). The analysis of multiple incident and weather scenarios 

Title: Benefit-Cost Analysis of Freeway Service Patrol Programs: Methodology and Case Study

Objective: The objective of this study was to estimate the benefits of a Freeway Service Patrol (FSP), 

the Highway Emergency Local Patrol (H.E.L.P.) program operating in New York State. 

Type of Tool/Analysis Used: A CORSIM-based simulation methodology was applied for estimating the 

benefits of the H.E.L.P. program. The benefits assessed include savings in travel delay, fuel consumption, 

emissions, and secondary incidents. Using this methodology, the monetary equivalent of these savings 

was computed to obtain an estimate of the benefit-to-cost (B/C) ratio.

Results: This study showed that the H.E.L.P. program operated with better than two-to-one B/C ratio. If 

vehicle occupancy, traffic composition with commercial vehicles, and the benefit of avoiding secondary 

incidents were considered, the B/C ratio could be in a range of 3.4 to 4.2. If considering fatal incidents 

that were avoided, this ratio would increase to between 13.2 and 16.5.
21

(where a variety of incident types can occur at multiple times and locations) were strictly limited to 

manage analysis costs.

The analysis found significantly positive benefit/cost ratios for integrated corridor management strategies, 

which include incident management.

This analysis approach can evaluate a wide variety of incident management strategies but it requires a 

significant investment in analysis effort for the various models that must be employed.

More information on the ICM analyses methods and results can be found at:

http://www.its.dot.gov/icms/success_icme.htm.

Table 1. ICM TIM Modeling Tools

Model Type Minneapolis Dallas San Diego

Regional Travel 

Demand Model

Metro model in TP+ NTCOG model, TransCAD TransCAD

Mesoscopic 

Simulation Model

Dynus-T – 

supported by 

University of Arizona

DIRECT – 

supported by Southern 

Methodist University

None

Microscopic 

Simulation Model

None None Transmodeler/Micro

(Source: Adapted from: http://www.its.dot.gov/icms/success_icme.htm.)

FHWA ATDM Evaluation Guide

The FHWA Active Transportation and Demand Management Evaluation Guide (ATDM Guide) and future 

replacement for Chapter 35 of the 2010 Highway Capacity Manual currently is under preparation. The 

ATDM Guide recommends the creation of three different prototypical incident scenarios (no incident, one 

lane blocked, two lanes blocked) for each of good weather and bad weather days for a total of 6 capacity 

scenarios. The 6 capacity scenarios are each matched with 5 different levels of demand. The result is 

30 scenarios for evaluating incident management and other ATDM strategies.

Special demand, capacity and speed adjustment factors currently are being developed to reflect the 

effects of incidents and various incident management strategies on these factors. The method will 

be sensitive to traveler information strategies, speed control strategies (VSL), and lane management 

strategies (temporary shoulder lane use, etc.).

Once the scenarios have been created and the demand/capacity/speed adjustment factors computed, 

conventional HCM methods are then used to evaluate facility performance.

The HCM predicted performance for each scenario is weighted by the probability of the scenario 

occurring over the course of a year to obtain average, median, and any desired percentile (e.g., 95th

percentile) result.

Software to implement aspects of the ATDM methodology is being developed. The proposed ATDM 

evaluation methodology is being tested on the I-15 corridor in San Diego.
22

SHRP 2 Projects Relevant for Incident AMS

Several completed and ongoing SHRP 2 projects deal specifically with the prediction of travel 

time reliability, of which incident impacts are a major component. These SHRP 2 projects are 

discussed below. 

More information on this methodology can be obtained from SHRP 2 staff. 

SHRP 2-L08 Incorporation of Reliability in the HCM

The SHRP 2-L08 Reliability Analysis Guide for the Highway Capacity Manual (Reliability Guide) 

currently is under preparation. The Reliability Guide will recommend the creation of several 

hundred to several thousand demand, weather, and incident scenarios to predict future travel time 

reliability distribution.

Two methods for generating scenarios are being considered. One enumerates all possible scenarios and 

selects the ones of most interest for more extensive evaluation. The other method uses a Monte Carlo 

approach to generate the scenarios. 

Special capacity and speed adjustment factors currently are being developed to reflect the effects 

of incidents (but not incident management strategies) on these factors. The SHRP 2-L08 project is 

focusing on predicting existing and future reliability under existing control conditions, rather than 

predicting how changes in operational strategies can affect reliability.

Once the scenarios have been created and the demand/capacity/speed adjustment factors 

computed, conventional Highway Capacity Manual (HCM) methods are then used to evaluate 

facility performance. 

An improved HCM Urban Streets method is being developed to better support reliability analysis on 

arterial streets. The improved method will be able to account for the impacts of queues on upstream 

signal operation.

The HCM predicted performance for each scenario is weighted by the probability of the scenario 

occurring over the course of a year to obtain average, median, and any desired percentile (e.g., 95th

percentile) result.

Software to implement the methodology is being developed. The proposed methodology will be tested 

on a half dozen freeway and urban street data sets.

SHRP 2 L03 Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies

SHRP 2 Project L03 produced two types of statistical equations based on empirical data for 

predicting reliability measures. The first set relates the mean congestion condition, as measured 

by the travel time index (TTI) to a variety of reliability metrics. A strong correlation was found 

between the mean and the rest of the reliability metrics used, including standard deviation, upper 

percentiles of the travel time distribution, and on-time measures. The second set relates reliability 

metrics to demand, capacity, incident blockage, and weather. Publication of the report is expected 

in late 2012.
23

Methods for Predicting Incident Duration

When an incident occurs, the timely estimate of its duration plays a key role in the overall incident 

management process. Reliable incident duration predictions can help traffic managers in providing 

correct and essential information to road users, applying appropriate traffic control measures 

at or near the incident location and evaluating the effectiveness of the incident management 

strategies implemented. The duration of an incident can have several definitions, depending on 

what is chosen for the start and end times of the incident. Generally the start time is when the 

incident is first detected by incident management personnel. Ideally the start time would be the 

time when the incident actually occurred, but this cannot be known with certainty. However, in 

urban areas, the time between actual start and detection is very small because most incidents are 

reported by travelers via cell phones within a very short time of the actual occurrence. The end 

time is usually selected as the time when all lanes are open to traffic or when the last responder 

has left the scene.

State of the Practice

In Maryland, a Rule-Based Tree Model (RBTM) was applied to develop the prediction model for freeway 

incident duration by Kim et al.18 The model was developed based on the Maryland State Highway 

(MDSHA) incident database. The overall confidence for the estimated model was over 80 percent. In 

cases where RBTM did not provide incident duration within a desirable range, a discrete choice model 

was developed as a supplemental model.

State of the Art

There are several methods that have been developed in predicting incident duration, as listed below:

• Regression model;

• Hazard-based duration regression model; 

• Log-logistic model;

• Prediction/decision tree model;

• Support/relevance vector machine model; and

• Bayesian network model.

Methods for Predicting Secondary Crashes

Secondary crashes are associated with vehicles in close proximity due to a queue formed from a 

primary incident, the abrupt “end-of-queue” condition caused by a primary incident, collisions with 

emergency vehicles and personnel, and rubbernecking in both the current and opposite directions of 

travel. Secondary crashes can be severe, especially at night when visibility is reduced and traffic 

queues are unexpected. Modeling methods to predict secondary crashes would be greatly enhanced 

if traffic management center personnel could flag crashes that occur in the queue caused by the 

primary incident or from opposite direction rubbernecking. Currently, researchers must derive these 

items analytically.
24

State of the Practice

Researchers at the Virginia Center for Transportation Innovation and Research also developed a dynamic 

queue-based tool to identify primary incidents (Secondary Incident Identification tool – SiT). They have 

used SiT and iMiT together to begin to improve the state of the art in modeling secondary incidents.19

State of the Art

The following is a list of methods that have been developed and used for quantifying the occurrence and 

characteristics of secondary crashes.

• Regression model;

• Ordered logit model;

• Probit model;

• Logistic regression model; 

• Bayesian network model; and 

• Simulation-based secondary incident filtering method.

For instance, a study conducted by Zhan et al. used a comprehensive incident database on I-95 from 

District 4 of the Florida DOT to identify freeway secondary crashes and their contributing factors.20 A 

method based on a cumulative arrival and departure traffic delay model was developed to estimate the 

maximum queue length and the associated queue recovery time for incidents with lane blockages. 

Vlahogianni et al. also recently utilized neural networks and statistical approaches to study the impact of 

weather on secondary crashes.21 Their findings were that speed, volume, number of blocked lanes and 

vehicles involved in a crash significantly influence the probability of a having a secondary incident. 

A compendium of how transportation agencies are dealing with secondary incidents can be found in the 

document: Traffic Incident Management Performance Metric Adoption Campaign. 
25

Predicting Incident Characteristics (Independent Variables) 

Incident models are built using indicators of incident performance as the predictor variables (e.g., 

incident duration, lane-hours lost due to incidents). Knowing how TIM strategies affect these 

independent variables is therefore of utmost importance. A number of studies have been done over the 

past two decades that can be used for this purpose. SHRP 2 L03 assembled the most recent studies in 

this area. 

EVALUATION OF TIM AMS METHODS

This subsection provides a qualitative assessment of incident modeling methods based on selected criteria. 

Qualitative Assessment Based on Selected Criteria (Review Matrix)

Table 2 shows a comprehensive assessment of incident modeling methods.

Title: Primary and Secondary Incident Management: Predicting Durations in Real Time

Objective: The main objectives of this study were to analyze the occurrence and nature of secondary 

incidents in the Hampton Roads (HR) area in Virginia, and develop tools that can analyze primary and 

secondary incidents at the planning and operational levels. 

Type of Tool/Analysis Used: A dynamic queue-based tool, Secondary Incident Identification Tool (SiT), was 

developed to identify primary and secondary incidents from historical incident data. An on-line tool, iMiT, was 

developed to predict the remaining duration of an existing incident, the chances of a secondary incident based 

on the characteristics of the primary incident, and the associated delays. 

Results: This study found that secondary incidents account for nearly 2.0 percent of Transportation 

Operations Center (TOC)-recorded incidents, using the 2006 data. Of all accidents, 7.5 percent had 

associated secondary incidents, 1.5 percent of disabled vehicles had secondary incidents, and 0.9 

percent of abandoned vehicles had secondary incidents. The average duration of secondary incidents in 

Hampton Roads was 18 minutes, which was 4 minutes longer than the mean duration of other incidents, 

indicating that secondary incidents were not necessarily minor “fender benders.” The study also found 

that a 10-minute increase in primary incident duration was associated with 15 percent higher odds of 

secondary incidents.
Table 2. TIM Review Matrix

26

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Measuring Impacts of Incidents on Traffic FlowData Collection and Archiving (Incident and Travel Time Data)Roadway Sensors/Detectors

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Easy Many Unknown N/A • Detector health

• Spot measurement 

(not continuous)

Good for Empirical-Based Statistical Analysis

• • • • • • 

Freeway Service Patrol (FSP)

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Moderate Many Unknown N/A • Not all incidents 

are included as FSP does not respond to all incidents

Good for Empirical-Based Statistical Analysis

• • 

Accident Logs

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Moderate Many Unknown N/A • Not all incidents 

are included in accident logs and some of the records are not accurate

Good for Empirical-Based Statistical Analysis

TMC Data Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Moderate Many Unknown N/A • May contain 

incomplete information

Good for Empirical-Based Statistical Analysis
Table 2. TIM Review Matrix (continued)

28

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Data Collection and Archiving (Incident and Travel Time Data) (continued)Simulation Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Difficult Many Unknown N/A • Resource intensive 

(cost, expertise, analysis time)

Good if the simulation model is developed and calibrated well

• • • • • 

• 

Automatic Number Plate Reader (ANPR)

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Difficult Rare Unknown N/A • Resource intensive 

(cost and analysis time)

Good for Empirical-Based Statistical Analysis

• 

Web-basedData Collection and Archiving System

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Extensive Easy Some Unknown N/A • Unknown Good if the 

quality of data feeding into the system is good

• 

Analytical MethodsCoordinated Highways Action Response Team (CHART, University of Maryland, Model-Based Stochastic Approach)

Y Y Y Y Y Y Y Y Minimal Easy One Unknown N/A; 

Statistical-based

• Only includes a 

volume term – should include a V/C term instead

Will overestimate delay at low volumes

• 
Table 2. TIM Review Matrix (continued)

30

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Analytical Methods (continued)Quantile Regression (EmpiricalBased Statistical Method)

Y Y Y Y Y Y Y Y Y Y Extensive Easy One Unknown N/A; 

Statistical-based

• It is site specific 

and hard to make generalization to other facilities

Results are valid as long as data input is reasonably accurate

• 

Simulation Y Y Y Y Y Y Y Y Y Y Extensive Difficult One Unknown N/A; 

Simulation-based

• Unknown Moderate • 

• • • 

Predicting Impacts of Incidents on Traffic FlowRegression Model

Y Y Y Y Y Y Y Y Y Y Y Extensive Easy Two Unknown N/A; 

Statistical-based

• N/A Good • 

• 

Shock Wave Model

Y Y Y Y Y Y Y Y Y Y Moderate Moderate One Unknown Yes • Tend to report 

shorter recovery time as it only calculates queue dissipation time which does not necessarily equate with the time to return to pre-incident normal traffic flow condition

Moderate • 
Table 2. TIM Review Matrix (continued)

32

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Predicting Impacts of Incidents on Traffic Flow (continued)Queuing Analysis

Y Y Y Y Y Y Y Y Y Y Y Moderate Easy Three Moderate 

validation efforts

Yes • Unknown Moderate • 

• • 

Adjustment Method Based on Queuing Analysis

Y Y Y Y Y Y Y Y Y Y Extensive Moderate One Moderate 

validation efforts

Yes • Unknown Moderate • 

Differencein-TravelTime Method

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One Moderate 

validate efforts using a case study

N/A; Statistical-based

• Unknown Moderate • 

Marginal Incident Computation (MIC) Model 

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult One Unknown N/A; 

Statistical-based

• Model is 

complicated

• Should be refined 

to consider other causes of variable travel times, such as demand fluctuations and capacity fluctuations

Moderate • 

IDAS Y Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate N/A Unknown Partly (Combined 

analytical model and empirical data)

• Do not 

consider spatial characteristics of incident delay

Moderate • 
Table 2. TIM Review Matrix (continued)

34

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Predicting Impacts of Incidents on Traffic Flow (continued)Genetic Neural Network (GNN)

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult One Unknown N/A; 

Statistical-based

• Difficult to 

understand the model parameters (blackbox)

Moderate • 

Cellular Automata

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult Two Moderate 

validation efforts

Yes • Computation 

resource intensive

• May give poor 

results if input detector data is not accurate

Moderate • 

• 

Simulation Y Y Y Y Y Y Y Y Y Y Y Extensive Difficult Five Unknown N/A; 

Simulation-based

• Resource intensive 

(data, cost, expertise, analysis time)

Good • 

• • • • 

• • 
Table 2. TIM Review Matrix (continued)

36

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Predicting Incident Characteristics (e.g., Duration)Regression Model

Y Y Y Y Y Y Y Y Y Y Y Moderate Easy Three Moderate 

validation efforts

N/A; Statistical-based

• The explanatory 

power of the model may be poor

Moderate • 

• • 

Log-Logistic (Accelerated Failure Time, or AFT) Model

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One Outperformed 

other naïve predictors

N/A; Statistical-based

• Unknown Moderate • 

iMiT - Incident Management Integration Tool (On-line Tool Based on Statistical Regression)

YYYa Y Y YYb Y Y Y Y Moderate Easy One Empirically 

validated by comparing the model's predicted incident durations in year 2007 against the observed incident durations 

N/A; Statistical-based

• The model 

was based on Safety Service Patrol (SSP) data, but SSP did not respond to all incidents; therefore, the data used for the model may be biased

Good (Considering it was able to predict incident duration with root mean squared error (RMSE) within 16.4%

• 

Shock Wave Model

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One N/A Yes • Unknown Moderate • 

Hazard-Based Duration Regression Model 

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One N/A N/A; 

Statistical-based

• Unknown Moderate • 

Prediction/Decision Tree (DT)

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One Moderate 

validation efforts

N/A; Statistical-based

• Unknown Moderate • 
Table 2. TIM Review Matrix (continued)

38

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Predicting Incident Characteristics (e.g., Duration) (continued)Rule-Based Tree Model (RBTM)

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate One N/A N/A; 

Statistical-based

• May need to use 

together with supplemental models for more accurate prediction

Moderate • 

• 

Support/Relevance Vector Machine (RVM) Model

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult One Moderate 

validation efforts

N/A; Statistical-based

• Tend to 

underestimate the prediction values for the long duration incident cases

Moderate • 

Hybrid Model (Rule-Based Tree Model (RBTM), Multinomial Logit Model (MNL), and Naïve Bayesian Classifier (NBC))

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult One N/A N/A; 

Statistical-based

• Model is 

complicated 

Moderate • 

K-NearestNeighbor (KNN)

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult Two Moderate 

validation efforts

N/A; Statistical-based

• Tend to 

overestimate the prediction values for the short duration incident cases

Moderate • 

• 

Artificial Neural Network (ANN)

Y Y Y Y Y Y Y Y Y Y Y Moderate Difficult One Moderate 

validation efforts

N/A; Statistical-based

• Tend to 

overestimate the prediction values for the short duration incident cases

Moderate • 
Table 2. TIM Review Matrix (continued)

40

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Predicting Incident Characteristics (e.g., Duration) (continued)Bayesian Network

Y Y Y Y Y Y Y Y Y Y Y Moderate Moderate Two Unknown N/A; 

Statistical-based

• It is site specific 

and hard to make generalization to other facilities

Good • 

• 

Quantifying Occurrence and Characteristics of Secondary CrashesRegression Model

Y Y Y Y Y Moderate Moderate One The model 

was validated against 640 sample data set and the result showed that the methodology reduced Type I error by 24.38% and Type II by 3.13%

N/A; Statistical-based

• Unknown Moderate • 

Ordered Logit Model and Heckman Model

Y Y Y Y Y Y Y Y Y Moderate Moderate One N/A N/A; 

Statistical-based

• Model has limited 

goodness of fit due to the complexity and randomness of secondary incident occurrence

Moderate • 
Table 2. TIM Review Matrix (continued)

42

Category

Application

Data 

Requirements

Ease 

of Use

Amount of 

Applications 

in Practice

Validation 

Efforts

Consistency 

with Traffic 

Flow Theory Known Shortcomings

Qualitative 

Assessment 

of Validity 

Development and Evaluation of TIM Plans

Analysis and Valuation of TIM Strategies

Decision Support Systems (On-line/Off-line)

Incident Prediction and Detection

Incident Duration Prediction

TIM Performance Measures

Relationship between TIM and Overall 

Congestion/Travel Time Reliability

Benefit-Cost Analysis of TIM Programs/ 

Strategies

Safety Analysis Applications 

Real-Time ATIS 

Integrated Corridor Management

Appropriate for Long-Range Planning

Appropriate for Corridor Planning

Appropriate for Deployment Planning

Appropriate for Benefit/Cost Analysis of Results 

Quantifying Occurrence and Characteristics of Secondary Crashes (continued)SimulationBased Secondary Incident Filtering (SBSIF) Method 

Y Y Y Y Y Y Y Y Y Moderate Difficult One Validated 

using 6-month data along a segment of I-287 in the New York State

N/A; Simulation-based

• Model needs to be 

recalibrated for use for other locations

• Additional 

factors, such as weather, could be considered

Moderate • 

Probit Model Y Y Y Y Y Y Y Y Y Y Moderate Moderate Two N/A N/A; 

Statistical-based

• Model has limited 

goodness of fit due to the complexity and randomness of secondary incident occurrence

Moderate • 

• 

Logistic Regression Model

Y Y Y Y Y Y Y Y Y Y Moderate Moderate Two Unknown N/A; 

Statistical-based

• Unknown Moderate • 

• 

Bayesian Network 

Y Y Y Y Y Y Y Y Y Y Moderate Moderate One Moderate 

validation efforts

N/A; Statistical-based

• Unknown Moderate • 

NotesA: Development and evaluation of TIM plans.B: Analysis and valuation of TIM strategies such as use of 

service patrols.

C: Decision support systems (on-line/off-line).D: Incident prediction and detection.E: Incident duration prediction.F: TIM Performance Measures.G: Relationship between TIM and overall congestion/travel 

time reliability.

H: Benefit-cost analysis of TIM programs/strategies.I: Safety analysis applications such as secondary 

crash analysis.

J: Real-time ATIS. K: Integrated Corridor Management.L: Most appropriate uses of the method (e.g., long-range 

planning, corridor planning, deployment planning, benefit/cost analysis).

a On-line.b Predict secondary incident occurrence.
44

INCIDENT DATA REQUIRED TO SUPPORT TIM AMS APPLICATIONS

TIM PERFORMANCE MEASURES

Monitoring trends in performance leads to sound investment in transportation strategies and policies. 

Performance programs have evolved over the years to include many types of performance measures 

that are used to manage incident programs. Recently, there has been a move to developing a few core 

measures that can be used as indicators of program performance for all transportation agencies, while 

recognizing that additional measures are needed at the agency level. In TIM, a national effort22 has 

produced three of these core measures:

• Roadway clearance time is the time between first recordable awareness of an incident (detection/

notification/verification) by a responsible agency and first confirmation that all lanes are available 

for traffic flow. 

• Incident clearance time is the time between the first recordable awareness and the time at which 

the last responder has left the scene. 

• Secondary crashes are those that occur with the time of detection of the primary incident where 

a collision occurs either a) within the incident scene or b) within the queue, including the opposite 

direction, resulting from the original incident. 

Additional TIM performance measures are of use to agencies beyond the core measures. Monitoring 

other parts of the incident “timeline” also can lead to identifying and correcting program deficiencies. 

Incident locations are used to identify hotspots. Identifying types of incidents (e.g., crashes, vehicle 

disablement, hazardous material release, large truck involvement) also produces actionable information. 

DATA COLLECTION 

The incident data to support the development of performance measures are the same that are required to 

support TIM modeling applications and includes the types of data shown in Figure 5. 

Incident Characteristics – This data type includes elements that describe the number, location, 

characteristics, and duration of incidents. The most important data elements for the research are:

• Incident type (crash, disabled vehicle, fire, debris, abandoned vehicle).

• Incident collision type (fixed object, overturn, vehicle/side, vehicle/head-on, vehicle/rear-end).

• Incident “timeline” data (time stamps for: start of incident, detection, verification, on-scene 

arrival, lane/shoulder open, all clear).

• Incident severity (KABCO injury scale).
45

Figure 5. Incident Data Model for Performance Monitoring

(Source: Cambridge Systematics, Inc.)

• Incident location (route, travel direction, milepost or GPS coordinates).

• Incident blockage.23

» Cross section feature affected (lane, partial lane, right shoulder, left shoulder, median, off 

maintained way);

» Lane type (through/GP, through/HOV, auxiliary, on-ramp, off-ramp); and

» Begin/end time.

• Number of involved vehicles.

The duration of the blockages caused by incidents is the most important piece of information 

needed for research, but others also affect traffic flow conditions. Also, some incident data may be 

incomplete and the type of incident may be used as an indicator of the lane closure impacts (e.g., for 

data checking purposes).

Possible sources of data include the following:

• Service Patrol/Incident Response Team Data Entry – PDAs/laptops in the vehicles;

• Automated sensors (for detecting incident start times);

• Freeway service patrols (on-scene reporting);

• Law enforcement officer on-scene reports;

• TMC Operator Data Entry – Automate the conversion of web information to database entries; and

• Police Computer-Aided Dispatch (CAD) files.
46

Incident Activity Data – This data type relates to the nature of how incidents are managed by an agency. 

Having this type of data is not strictly necessary for doing incident modeling, as they are rarely used as 

direct input variables to modeling procedures. However, incident activity data can influence incident 

characteristics. For example, increasing the number of service patrols can reduce incident response and onscene management times. Many past studies have established links between incident activities and incident 

characteristics; the Joint Program Office’s ITS benefits database has a compilation of these relationships:

http://www.itsbenefits.its.dot.gov/.

Examples of the incident activity data include:

• Incident detection algorithms (used with traffic sensors);

• Method of incident notification to all responders (communication between agencies);

• CCTV coverage (percent of study segment capable of being viewed with CCTV);

• Degree of wireless communication for on-scene management;

• Service patrol vehicles per centerline mile by time period;

• Quick clearance laws or policies;

• Length of time abandoned vehicles are allowed to remain on a freeway shoulder (assuming they 

are not an imminent hazard);

• Laws or policies regarding the removal of stalled or abandoned vehicles from freeway shoulders;

• Policies and procedures to facilitate quick removal of heavily damaged vehicles and nonhazardous 

cargoes; and 

• Procedures for removing crash victims from incident scene.

Other data inputs to models and analytic procedures – Table 3 lists some of the other data inputs needed 

to run the various scales of AMS procedures.
47

Table 3. Input Requirements of TIM AMS Procedures

Model Type Network Demands TIM Calibration

Sketch 

Planning Models

Minimal. Generally 

deals with regional 

VMT and VHT.

Minimal. Generally 

regional VMT.

General categories 

of TIM strategies. 

No specific 

implementation 

details.

Not applicable.

Deterministic 

(HCM Type 

Macroscopic) Models

Link and intersectionspecific lane 

geometry, speed 

limits, controls 

(signal timing).

Link and intersectionspecific hourly 

demands by vehicle 

type (usually just 

peak hour).

Incident types 

(number of lanes 

blocked, specific 

links affected, and 

average duration). 

Strategies to be 

tested and 

expected effects 

on average lane 

blockage durations.

Not generally done.

Mesoscopic 

Simulation Models

Same as 

deterministic HCM.

OD tables by hour of 

day for peak periods.

Same as for 

deterministic HCM. 

Observed flows 

and link speeds.

Microscopic 

Simulation Models

Same as HCM plus 

signal detector 

locations, signal 

controller settings, 

turn pocket lengths.

Same as mesoscopic 

or link and 

intersection-specific 

demands by 

vehicle type.

Incident start/end 

times, longitudinal 

location within link. 

Expected effect of 

strategies on specific 

incident duration.

Observed flows 

and link speeds.

DEFAULT DATA FOR TIM AMS APPLICATIONS

In the absence of agency-specific data, data developed from other areas may be used for TIM AMS 

applications, especially for forecasting purposes. Some of these data represented below. SHRP 2 Project 

L08 is developing a larger library of these data.

A major caveat in the use of these data is that they were developed from areas where active incident 

management programs already exist. Thus, the incident duration data in particular may not be 

representative of a “before” case where active TIM does not exist. 

For planning applications, it is often necessary to predict the number of future incidents. This can be 

done in a number of ways, including the following methods:
48

• Use of Incident Rates. If the analyst has the current incident rates (incidents per vehicle-miles of 

travel, VMT), either for a specific facility or as an areawide default, the number of incidents can 

be estimated simply by multiplying the incident rate by the forecasted VMT.

• If overall incident rates are not available, but a crash rate is, the crash rate can be factored up to 

total incident rates using agency-developed default values. If these default values are unavailable, 

the national default factors being developed by SHRP 2 Project L08 can be used. In the interim, a 

factor of 5.0 can be used, based on analysis of Atlanta data that showed crashes are roughly 20 

percent of all incidents.

• If accident rates are not available, the analyst should use the procedures in the Highway Safety Manual 

to estimate the number of crashes, convert this to a crash rate, then factor up to a total incident rate.

Table 4. Lateral Locations of Incidents

Atlanta, Georgia

Incident Type 

Frequency

Row Percent 

Col Percent 1 Lane 2 Lanes 3+ Lanes Off Roadway Shoulder Total

Accident 2,566

44.07

28.32

1,451

24.92

69.19

1,052

18.07

84.77

41

0.70

21.47

712

12.23

4.39

5,822

Stall 4,411

21.81

48.68

261

1.29

12.45

100

0.49

8.06

101

0.50

52.88

15,349

75.90

94.72

20,222

Debris 2,047

77.30

22.59

382

14.43

18.22

87

3.29

7.01

47

1.77

24.61

85

3.21

0.52

2,648

Roadkill 37

35.92

0.41

3

2.91

0.14

2

1.94

0.1

2

1.94

1.05

59

57.28

0.36

103

Total 9,0612,0971,241 191 16,205 28,795
49

Table 5. Incident Duration by Incident Type

Atlanta, Georgia

Lateral 

Location Frequency

Duration (Minutes)

Mean

Standard 

Deviation Median

95th 

Percentile

Accident 1 Lane 2,566 38.942 28.526 34.700 89.367

2 Lanes 1,451 46.279 30.697 41.283 96.450

3+ Lanes 1,052 66.718 56.219 56.233 149.767

Off Roadway 41 14.377 20.580 4.267 71.817

Shoulder 712 40.112 34.306 34.375 91.800

TOTAL 5,822 46.287 38.129 40.033 106.067

Stall 1 Lane 4,411 29.055 31.382 24.100 72.083

2 Lanes 261 40.650 44.884 33.917 109.883

3+ Lanes 100 40.002 23.263 38.850 76.800

Off Roadway 101 30.753 28.419 25.050 97.533

Shoulder 15,349 41.506 29.356 35.483 96.300

TOTAL 20,222 38.996 30.337 32.917 93.350

Debris 1 Lane 2,047 16.588 21.955 11.408 49.000

2 Lanes 382 17.789 23.002 11.833 51.067

3+ Lanes 87 45.448 62.059 22.833 183.633

Off Roadway 47 5.027 4.091 2.717 10.567

Shoulder 85 35.713 46.699 19.958 175.883

TOTAL 2,648 18.797 27.375 11.833 61.433

Roadkill 1 Lane 37 47.064 57.934 32.650 226.883

2 Lanes 3

3+ Lanes 2 2.325 1.473 2.325 3.367

Off Roadway 2 20.650 20.650 20.650

Shoulder 59 33.922 63.202 8.575 121.850

TOTAL 103 36.292 60.359 13.858 135.317
50

FUTURE IMPROVEMENTS IN TIM AMS APPLICATIONS

Based on the current state of the practice, several areas are ripe for further development in Traffic 

Incident Analysis Modeling and Simulation (TIM AMS). We have developed the following problem 

statements that should be considered for further research.

Project statements that are based on the priority area topics identified in the practitioner survey include 

the following:

• Incident Data (Standards, collection procedures, coordination with transportation agencies, etc.) – 

Covered in Topic 1;

• Incident Performance Measures (metrics, presentation, formats/reporting) – Covered in Topic 1;

• Technical Methods for Predicting Incident Congestion Extent (delay, reliability) – Topic 2 covers 

estimating incident delay and its contribution to total delay;

• Technical Methods for Predicting Incident Duration – Covered in Topic 3; and

• Technical Methods for Predicting Incident Crashes – Secondary Crashes covered in Topic 4.

Additional Topics were identified based on a review of current practice and research.

TOPIC 1 – DATA MANAGEMENT/DATA DICTIONARY FOR ARCHIVING THE DATA 

NEEDED TO COMPUTE INCIDENT PERFORMANCE MEASURES

This effort would cover data collection and processing so that all agencies are calculating the same 

measures, ensuring that agencies could be compared meaningfully. One of the key items is the ability 

to track changes in a single incident over its duration – blockages and response characteristics can 

change – and capturing this allows more detailed analysis of strategies and more realistic estimates 

of delay to be modeled. This effort could be extended to cover other forms of disruptions, primarily 

work zones. 

Purpose: The aim is to ensure a consistent process of data collection and archiving in order to enhance 

analysis and comparison of data across agencies. 

Impact on practice: A consistent dictionary of terms and data collection procedures will significantly 

enhance and ease the level of effort involved in conducting analysis using incident-related data. 

Practitioners also will be able to communicate with each other more effectively as key words and 

terminology will mean the same across agencies. 

Action: A series of reports modeled in a manner similar to the FHWA Traffic Analysis Toolbox to develop 

terminologies, criteria to be used in collecting data, and guidelines for archiving the data will be effective 

in setting up guidelines for archiving incident data.

How topic was developed: This topic was developed based on responses from the TIM survey. 

Agencies surveyed identified incident data standards, collection, and coordination as one of the top five 

priorities for which technical guidance would be helpful.
51

TOPIC 2 – LINKING INCIDENT MANAGEMENT STRATEGIES TO INCIDENT MODEL INPUTS 

The ATDM/HCM project will lay out a conceptual framework for evaluating the traffic performance 

benefits of various incident management strategies, however; this work is based on very limited data 

to calibrate and validate the specific parameters and sensitivities included in the framework. Additional 

work is required to add additional strategies to the framework and to calibrate and validate the specific 

models in the framework. This type of information is valuable for all levels of modeling, including the 

new work under SHRP 2 L08, which is not carrying the analysis back to this level. 

The need to understand how actions affect the inputs to the modeling process is heightened by the 

emergence of ATDM strategies. Some of these strategies are relatively new (e.g., lane control) while 

some have a longer history (e.g., ramp metering and incident management). These strategies are 

implemented dynamically and in combinations. Determining the conditions under which the strategies are 

triggered is key to modeling their effect. That is, having a “control plan” that replicates how operators 

will deploy ATDM strategies is required for effective modeling.

Purpose: To improve the confidence of the profession in the ATDM/HCM tools for evaluating TIM 

strategies and to increase the variety of TIM strategies that can be evaluated using the ATDM/

HCM tools. 

Impact on practice: This research will enable agencies to better understand the performance benefits 

of implementing various levels of TIM strategies and be able to make better decisions as to investments 

in TIM. 

Action: This topic may take advantage of the SHRP 2 research database to obtain data on traffic 

performance during incident conditions. Developing data on more advanced TIM strategies, not in the 

SHRP 2 database may require field data collection, preferably as “before/after” studies. 

How topic was developed: This topic was developed based on relevant information gathered from 

ongoing research on ATDM and travel time reliability (SHRP 2).

TOPIC 3 – REAL-TIME MANAGEMENT AND PREDICTION METHODS

The ability to forecast an incident impact immediately after an incident occurs, given its location and 

the state of the network, is crucial to advanced operational control strategies. This study will develop 

methods to predict short-term impacts of a range of incident scenarios and develop recommended 

mitigation strategies of each scenario. The combined forecasting and mitigation models should be 

designed so they can be integrated into operational incident management programs. 

Purpose: The aim is to give operators of incident management programs and traffic management centers 

(TMC) tools they can use to improve their response to incidents. 

Impact on practice: This project will help develop clear procedures that operators of incident programs 

can apply to effectively respond to incidents. It also will ensure a more consistent response pattern for 

day-to-day operations of TMCs. 
52

Action: This project should be conducted in two phases. The first phase should be a state-of-thepractice study on how incident management programs currently are predicting impacts of incidents and 

the steps they take in responding to incidents. The second phase should use historical data to develop 

the models that forecast incident impacts and develop mitigation strategies. The models developed in 

Phase 2 should be tested in real-time scenarios at TMCs.

How topic was developed: This topic was developed based on responses from the TIM survey. Incident 

prediction and prediction of incident duration were the two top items for technical guidance identified by 

agencies participating in the TIM survey that wanted technical guidance.

TOPIC 4 – IDENTIFY KEY FACTORS THAT CAUSE SECONDARY CRASHES

This effort would define a standard methodology for identifying secondary crashes based on identifying 

the key mechanisms that would cause secondary crashes. These include queue location at the time of 

crash, rubbernecking in the vicinity of the crash for both directions, and crashes involving emergency 

vehicles on their way to manage the primary incident. 

Purpose: This method would abandon crude assumptions of where and when a queue is present and use 

travel time and volume data to identify the queue empirically. 

Impact on practice: The improved and more credible process of identifying secondary crashes will make 

it easier to conduct analysis on the impact of TIM programs on secondary crashes and also assess the 

impacts of secondary crashes (delay, queuing etc.). 

Action: This will involve extensive historical or real-time data at crash sites with secondary crashes. The 

data collection should involve the factors that are likely to contribute to the secondary crashes, geometric 

location of the crash and secondary crash, queue length, flow of traffic in opposite direction of travel 

(rubbernecking), emergency vehicles are a few. Microsimulation modeling could be used to validate the 

models developed.

How topic was developed: Agencies participating in the TIM survey identified the need for improved 

technical methods for predicting secondary incident crashes.

TOPIC 5 – MODELING PROGRESSIVE LANE OPENING

It is very common for the lane blockage characteristics of an incident to change over the course of 

its duration. This can go both ways: responders may close additional lanes to facilitate clearance 

and involved vehicles may be moved. Recently, the idea of systematically opening lanes as clearance 

progresses, rather than waiting to open all lanes at once, has gained acceptance as a TIM practice. How 

this practice can be modeled remains an open question, and data on what are reasonable values for the 

duration of each successive-lane opening need to be developed. 

Purpose: Develop improved approach to implementing progressing-lane opening strategies. 
53

Impact on practice: Practitioners will have improved tools to assess the impacts of progressive-lane 

opening strategies and will be able to better assess the performance of such implementations and 

whether it is having the intended impact of improving traffic flow. 

Action: Given that some agencies already are implementing the practice, an NCHRP Synthesis study is 

suggested to survey the agencies and find out their current practices and analysis methods. This could 

be followed up with a full NCHRP study. 

How topic was developed: This topic was developed because the literature scan showed a few agencies 

have begun implementing progressive lane opening after incident response. A synthesis of current 

practice will be useful in assisting other agencies that may be interested in implementing this strategy.

TOPIC 6 – UPDATE OF HCM SECTIONS REGARDING INCIDENT MANAGEMENT AND 

CAPACITY LOSS

The data presented in the HCM on capacity loss is based only a few older studies and is not sensitive to 

any incident management strategies. These values are in need of updating with the improved traffic and 

incident data now available. 

Purpose: Provide practitioners with more credible information and factors for conducting incident analysis. 

Impact on practice: The updated values will ensure analyses conducted with the HCM are more 

acceptable to all stakeholders and analysts will be more confident about their results. 

Action: Given the TRB Highway Capacity and Quality of Service Committee is responsible for the HCM 

it will be best to develop an NCHRP problem statement to conduct the study to update the factors in 

the HCM.

How topic was developed: This update is needed because the Highway Capacity Manual is a key 

resource and practice reference for all transportation professionals. Having the Manual address Incident 

Management will be critical in ensuring techniques and best practices are adopted by the professional 

transportation community.

TOPIC 7 – ESTIMATING INCIDENT DELAY AND ITS CONTRIBUTION TO TOTAL DELAY

Past studies attempting to do this are somewhat limited and need to be extended. In addition to 

a methodology that could be applied on a case-by-case basis, simple predictive equations could be 

developed that relate incident delay to traffic and basic incident characteristics. 

Purpose: Improve the state of the practice in quantifying and analysis of incident impacts. 

Impact on practice: Ability to better quantify delay impacts due to incidents will create an opportunity to 

develop more targeted and effective measures to address these delays. 
54

Action: This will work well as an NCHRP research project. A key issue when modeling incident delay – as 

well as other nonrecurring causes of congestion – is how to account for recurring congestion. On facilities 

where a significant amount of recurring congestion occurs, the effect of incidents on total congestion is 

complex. If a lane-blocking incident occurs upstream of a bottleneck, the incident will meter flow into the 

bottleneck, reducing or eliminating the congestion it causes. In other words, the congestion cause has 

moved from the bottleneck to the incident location, i.e., “delay migration” has occurred. It is true that the 

incident-caused delay can be greater than the bottleneck-caused delay (depending on the number of lanes 

blocked), but accounting for the recurring delay that would have been there without the metering effect of 

the incident needs to be addressed.

How topic was developed: Agencies surveyed indicated incident prediction and duration were top on 

their list of topics for which they wanted additional technical guidance. This topic was developed to help 

analyze the impact of incident characteristics (location, severity, and duration) on delay.

TOPIC 8 – USE OF SIMULATION IN TABLETOP EXERCISES

This topic consists of customizing the user interfaces and analysis methods of one or more simulation 

models to facilitate their use by nonsimulation experts in the testing of TIM strategies in a tabletop 

exercise format. 

Purpose: To provide operators with tools for self-training and development of cost-effective TIM 

strategies tailored to their agency’s capabilities, objectives, and the characteristics of the incidents on 

their facilities. 

Impact on practice: This would result in a significant improvement in the day-to-day quality and 

effectiveness of agency responses to incidents. The agency also would have a greater depth of 

experienced personnel to place in charge of incident management. Agencies also would be able to use 

the tabletop exercises to explore various “what-if” scenarios. 

Action: Candidate simulation tools that can be readily modified into a tabletop exercise format must 

be identified. Speed of operation, sensitivity to TIM strategies, adaptability to tabletop exercise, and 

ease of use by nonexperts would be primary considerations. One or more cooperative simulation model 

developers may be partnered with to develop and test the necessary user interface modifications to 

facilitate rapid execution by personnel more familiar with incident management than simulation. Finally, 

the resulting model(s) must be tested with operations personnel to verify their ease of use by nonexperts 

and their ability to accurately reflect real world results.

How topic was developed: This topic was developed based on the review of the example application 

of TIM AMS strategies. The review showed that due to limited tools for incident analysis, most studies 

involve postprocessing data from a combination of simulation tools, travel demand models, and sketch 

planning programs (HERS/IDAS). Developing customized user interfaces and postprocessors will make it 

easier for nonexperts of the tools to understand TIM applications.
55

TOPIC 9 – FRAMEWORK DEVELOPMENT FOR TIM PROGRAM EVALUATION

Framework development for TIM program evaluation is necessary to assist operating agencies in correctly 

identifying and quantifying the costs and benefits of TIM programs. A structured framework also will 

ensure more consistent information reporting which will enable more collaboration and knowledge 

exchange and sharing between agencies. 

Purpose: To identify the inputs required and appropriate tools and analytical procedures for accurately 

evaluating the costs and benefits of TIM programs. 

Impact on practice: The accurate assessment of TIM costs and benefits will support the development 

and implementation of cost-effective TIM programs. 

Action: The primary and secondary benefits and costs of TIM and the variables affecting them must be 

identified. An economic, traffic operations, safety, and environmental assessment framework must then 

be developed.

How topic was developed: This topic was developed based on responses from the survey.

TOPIC 10 – EXPAND BENEFIT/COST ANALYSIS TO INCLUDE SECONDARY IMPACTS

This topic is an extension of the analysis framework discussed under the Topic 9. This topic expands 

primary B/C analysis to include secondary impacts of TIM programs, particularly the reduction in 

secondary crashes that can be a significant benefit of good TIM programs. 

Purpose: To identify and quantify the secondary impacts of TIM programs for use in benefit/

cost analyses. 

Impact on practice: Significantly improved assessment of the benefits of TIM programs, and improved 

decision-making regarding investments in TIM programs by considering a full range of expected impacts 

is expected. 

Action: The research would involve gathering data on the effects of TIM on secondary crashes, travel 

behavior, lost productivity, environment (emissions), insurance costs, etc. Data gathering would 

cover much longer periods to capture the likelihood of secondary accident occurrence. Models relating 

TIM strategies to secondary effects would be developed. A primer on the assessment of primary 

and secondary effects of TIM (such as the AASHTO Red Book – A Manual on User Benefit Analysis 

of Highway and Bus-Transit Improvements) would be produced to guide agencies in the benefit/cost 

assessment of TIM.

How topic was developed: This topic was developed because the TIM survey identified the need for 

improved technical methods for predicting secondary incidents and crashes. A more structured process 

is needed to include the secondary incidents in benefit/cost analysis.
56

TOPIC 11 – RESOURCE OPTIMIZATION FOR TIM PLANS

Incidents are, to an extent, random in nature. Planning to respond to them involves an element of 

estimating the number of incidents that will occur, where they will occur, the type of incident, and 

the level of severity. Agencies will deploy and position response teams based on estimates of these 

variables (probably from historical information) and the amount of resources (personnel, vehicles, tow 

trucks, etc.) they have. The TIM objectives will largely determine the incident response team, subject 

to the agency budget constraints. For example, the number of required patrol units at a given freeway 

section can be determined by the desired response time. Even in the case where there are adequate 

budgets, the process of determining the optimal location to position resources in order to respond 

effectively is not trivial.

Purpose: To develop procedures, analytical tools, and best practices that agencies rely on to plan, 

deploy, and coordinate their traffic incident management teams so they can respond effectively and clear 

the highest number of incidents with the minimal amount of resources.

Impact on practice: A well-developed planning tool and a guidebook with best practices will significantly 

ease the burden and reduce the level of guesswork smaller agencies and those with limited budgets face 

in running TIM programs. 

Action: A guidebook should be developed that includes an optimization tool and synthesis of best 

practice from agencies that have effective TIM programs.

The theoretical research needed to solve analytical resource allocation problems already exists in various 

branches of transportation literature and fields like industrial systems engineering. What is needed is the 

translation of the problem: the resources (service patrol staff and equipment, law enforcement agencies, 

first responders, etc.), constraints (limited budgets, area to be patrolled, communication staff) into an 

optimization system. The resource allocation tool developed should be developed in standard off-theshelf commercial software like Microsoft Excel. A user interface should be included to make it easy to 

use, including GIS and mapping software to facilitate deployment.

The best practice guidelines should cover practices at agencies in large metropolitan areas, suburbs, and 

rural areas to make the content useful to the diverse set of agencies that might use it.

How topic was developed: This topic was based on some of the challenges identified during the 

development of the report. We found agencies are still struggling with basic problems like the 

most optimal location for dispatching a freeway service patrol truck, or whether to have the truck 

drive up and down a corridor or position it. Such problems have already been solved in several 

areas and a guidebook will bring the solutions together in one place for managers of TIM programs.
57

ENDNOTES

1 FHWA, Best Practices in Traffic Incident Management, Report FHWA-HOP-100-50, September 2010.

2 Caltrans, Mobility Performance Report 2009, February 2011.

3 J. Kwon, et al., “Decomposition of Travel Time Reliability into Various Sources: Incidents, Weather, Work 

Zones, Special Events, and Base Capacity,” 2011 Transportation Research Board Annual Conference, Conference 

CD-ROM, 2011.

4 A. Skabardonis, K. Petty, and P. Varaiya, “Measuring recurrent and nonrecurrent traffic congestion.” In 

Proceedings of 82nd Transportation Research Board Annual Meeting, Washington, D.C., January 2003.

5 George List, John Falcocchio, Kaan Ozbay, Kyriakos Mouskos, Quantifying Non-Recurring Delay on New York 

City’s Arterial Highways, Region 2 University Transportation Research Center, New York, New York 2008.

6 http://ops.fhwa.dot.gov/trafficanalysistools/.

7 A. Khattak et al., iMiT: A Tool for Dynamically Predicting Incident Durations, Secondary Incident Occurrence, 

and Incident Delays, 2012 Transportation Research Board Annual Conference, Conference CD-ROM, 2011.

8 A. Torday, et al., Use of Simulation-Based Forecast for Real Time Traffic Management Decision Support: The 

Case of the Madrid Traffic Centre, European Transport Conference, 2008.

9 FHWA Guide on the Consistent Application of Traffic Analysis Tools and Methods, http://ops.fhwa.dot.gov/

resources/news/news_detail.asp?ID=787.

10 http://ops.fhwa.dot.gov/trafficanalysistools/.

11 http://www.fhwa.dot.gov/infrastructure/asstmgmt/hersfact.cfm.

12 http://ops.fhwa.dot.gov/trafficanalysistools/idas.htm.

13 http://www.fhwa.dot.gov/policy/2008cpr/appa.htm.

14 U.S. DOT-FHWA Transportation Asset Management Case Studies: Highway Economic Requirements System: 

The Oregon Experience.

15 http://www.fhwa.dot.gov/policy/2008cpr/.

16 http://www.fhwa.dot.gov/policy/2008cpr/.

17 E. Miller-Hooks, M. TariVerdi, and X. Zhang, Standardizing and Simplifying Safety Service Patrol Benefit-Cost 

Ratio Estimation: SSP-BC Tool Development Methodology, Technical Report, I-95 Corridor Coalition, 2012.

18 W. Kim, S. Natarajan, and G. Chang, Analysis of Freeway Incident Duration for ATIS Applications, 15th World 

Congress on Intelligent Transport Systems and ITS America’s Annual Meeting, 2008.

19 A. J. Khattak, X. Wang, H. Zhang, and M. Cetin. Primary and Secondary Incident Management: Predicting 

Durations in Real Time. Final Report VCTIR 11-R11, Virginal Center for Transportation Innovation and Research, 

April 2011.

20 C. Zhan, A. Gan, and M. A. Hadi. Identifying Secondary Crashes and Their Contributing Factors, In 

Transportation Research Record: Journal of the Transportation Research Board 2102, 2009.

21 E. I. Vlahogianni, Matthew Karlafits and Foteini Orfanuo, Modeling the Effects of Weather on the Risk of 

Secondary Incidents, 2012 Transportation Research Board Annual Conference, Conference CD-ROM, 2012.

22 http://ops.fhwa.dot.gov/publications/fhwahop10010/presentation.htm.

23 Ideally, blockage is updated every time blockage changes during a given incident.